Scilab for Data Mining

Python for Machine and Deep Learning

Scilab Courses

Scilab is an open source, cross-platform numerical computational package and a high-level, numerically oriented programming language. It can be used for signal and image processing, statistical analysis, Internet of Things, data mining, etc. In Trity Technologies we have developed more than 20 courses based on Scilab since last few years.

Raspberry Pi Courses

The Raspberry Pi is a series of credit card–sized single-board computers developed in the United Kingdom by the Raspberry Pi Foundation with the intent to promote the teaching of basic computer science in schools and developing countries. Our very first Raspberry Pi Training is the aplication in IoT, and we are extending the training into other fields from time to time.

E4Coder - Automatic Code Generation

E4Coder is a set of tools that can be used to simulate control algorithms and to generate code for embedded microcontrollers running with or without a realtime operating system. Our course focus on using the block diagram for algorithms development and the codes would be automatically generated and downloaded into the embedded boards such as Arduino Uno. A mobile robot application would be used for the training for practical hands-on.

Starting from machine learning, we move towards deep learning to give you details understanding on how and why it works!

“The Deep Learning Research Becomes More Effective Thanks to Open Source Softwares...”

Course Synopsis

Artificial Intelligence (AI) is any theory and development that make a machine thinks like human beings, which has trigger the interest of scientists and researchers since early 1950s. AI development includes the explicitly programmed algorithms, such as fuzzy logic and expert system, to the learning system, such as neural network.

Machine Learning is a subset of Artificial Intelligence, in which a system is focused on training system to perform tasks by giving the machine training data. The word “learning” define the scope of the systems in which the system would learn either by supervised or unsupervised training. The former systems include neural network and support vector machine, and the latter includes self-organizing map and various clustering approaches.

This training would focus in machine learning and then gradually guide participants to deep learning, which is the subset of the machine learning with more hidden layers in the network. The participants would go through the hands-on from the basic machine learning to deep learning. The training would be conducted mainly using Python, with the packages such as TensorFlow, Keras, and others.

Course Objectives

This is a hands-on application course that provides step-by-step description while concentrating on useful tips and tricks to machine learning and deep learning system. Participants will be introduced to various algorithms through practical sessions with plenty of Python code examples and exercises for real-world applications.

Who Must Attend

Lecturers, students, programmers, developers, engineers and simply anyone who would like to work on intelligence systems for their projects are encouraged to attend the course.

Prerequisites

Candidates must have experience with any programming language, preferably and with knowledge in statistics and linear algebra.

Course Outline

Day 1

Artificial Intelligence, Machine Learning, or Deep Learning?

This section will discuss the differences among artificial intelligence, machine learning, and deep learning. At the end of section, participants will setup their own laptop with the modules for the training.

Introduction to Artificial Intelligence and the current development

Introduction to various types of Machine Learning system

Introduction to Deep Learning and the current states and variants

Machine learning development tools for Python

Hands-on: Setting up Development Environment

Step-by-Step Guideline to work with Machine Learning Systems

This section will cover the flow while working with machine learning. The flow is important in order to achieve near-human performance.

Preparing Data

Selection and Evaluation of Model

Tuning the Hyperparameter

Bias–variance Tradeoff

Hands-on: Building Basic Machine Learning System

Supervised and Unsupervised Learning Systems

This section will give the participants an overview of supervised and unsupervised learning system. The remaining sections of the course will focus on supervised learning systems.